Editorial: Evidence-Based Practice and The Golden Quadrant

EVIDENCE-BASED PRACTICE AND THE GOLDEN QUADRANT

As evidence is translated into practice, three components interplay to produce the “best” healthcare intervention: the best evidence available, healthcare practitioner expertise, and patient characteristics and preferences. (Bliss-Holtz, 2007; Melnyk & Fineout-Overholt, 2005; Sackett, Rosenberg, Gray, Haynes & Richardson, 1976). What is produced by this process of evidence translation may be interventions for a single client or it may be algorithms, policies, and procedures for an entire patient population. Although the goal of the process is to produce excellent patient outcomes, this goal always comes with the delicate balancing by healthcare practitioners of the characteristics of the patient or patient population, the strength and level of the evidence, and the level of resource utilization that the practice change demands. It is these last two factors, the evidence and the available resources, that will be addressed in this article through the framework of the ACE Star model of knowledge transformation (Stevens, 2006) and the concept of the “Golden Quadrant.”

The ACE Star model of knowledge transformation uses five steps to convey the translation process: 1) generation of knowledge (Discovery phase); 2) production of a succinct summary of the evidence (Evidence Summary phase); 3) translation of the evidence summaries into clinical guidelines, protocols and algorithms (Translation phase); 4) planning and execution of an implementation process (Integration phase); and 5) monitoring of quality assurance data, with performance improvement processes deployed if outcome results do not match those predicted from the evidence (Evaluation phase). It is in the translation phase of the process that healthcare practitioners search for the Golden Quadrant; the place where the strongest evidence meets the lowest resource utilization. This is conceptualized by the diagram in Figure 1. The Golden Quadrant is easier to ignore when evidence-based decisions related to the care of one patient are being discussed, however when evidence is being translated into policies and procedures that affect entire patient populations, finding the Golden Quadrant becomes a more critical concern. Sometimes those searching for it can only balance the strength of the evidence with its cost through use of local cost-related data such as nursing personnel time, supplies, and equipment costs. Unfortunately, such “home-grown” estimates lack more sophisticated parameters like factoring in the “cost of doing nothing.” Fortunately, publications of medical and medical research increasingly include costs of tested interventions and compare these with the “cost of doing nothing.”

Figure 1. Searching for the Golden Quadrant.

An example of this is Hubbard's (2007) cost analysis of newborn screening for 29 genetic/metabolic disorders as recommended by the United States Health Resources and Services Administration (HRSA), which was based on findings from an American College of Medical Genetics study. The author performed a cost-consequence analysis using the highest- and lowest- ranking genetic/metabolic disorders (medium-chain acyl-CoA dehydrogenase deficiency [MCADD] and beta-ketothiolase [BKT], respectively). Hubbard examined the financial costs of the initial screening and resultant costs of confirmatory testing (for both true and false positives) as well the human costs of false negatives (which fortunately were rare) of both disorders. The resultant analysis provided both detailed evidence strength (in the form of sensitivity and specificity of current testing methods) and cost information (in the form of annual costs of true and false positives and false negatives in the state of Maryland) on which to base a Golden Quadrant decision.

Another example, although not performed with pediatric subjects, is a study by Pfefferkorn and associates (2009), who investigated prophylactic use of antibiotics at the time of urinary catheter removal in adults. The results of this randomized trial of 205 subjects included calculation of relative risk reduction and the number needed to treat as well as the cost of prophylaxis versus “doing nothing” in terms of drug costs (prophylaxis versus treatment costs in patients subsequently developing an infection), which amounted to a savings of approximately 22%. The cost savings did not take into account that the average cost per case of catheter-associated urinary tract infections, when they do occur, is estimated to be more than $44,000 (Hines & Yu, 2009).

Unfortunately, gathering cost data by which to make Golden Quadrant decisions is not always easy. For example, the difficulty of being able to compare differences in hand hygiene costs between 40 U.S. hospitals rated as “high” or “low” in hand hygiene guideline compliance (a practice certainly based on strong evidence) was described in a study by Stone and associates (2007). No significant difference was found in hand hygiene costs between high and low use settings, possibly because a limitation of the study was the “… quality and availability of data regarding costs of hand hygiene” (p. 283). Is it any wonder that healthcare professionals who need to financially justify an evidence-based practice change become overwhelmed in trying to do so when a study conducted by premier nurse scientists and funded by the National Institute of Nursing Research cannot be assured of receiving accurate financial data from healthcare institutions?

It could be said that the Golden Quadrant is a movable target, as evidence-based guidelines that are locally produced and accepted may be the ones that balance the “next best” evidence, which also happens to carry lower costs. When this occurs, consistent quality assurance monitoring becomes vital to assure that patient outcomes are not compromised, and that the projected improvement has occurred. However, until more accurate financial data can be retrieved from healthcare institutions, making decisions along the financial axis of the Golden Quadrant will continue to be a less than exact science.